---
title: K8s Manifest Generator
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/k8s-manifest-generator/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/k8s-manifest-generator/
last_updated: 2026-06-20
---

# K8s Manifest Generator

> Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and…

A step-by-step guide and template set for generating production-ready Kubernetes manifests: Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Every manifest follows cloud-native best practices with resource limits, health probes, security contexts, and standard labels built in.

## Use cases
- Creating Deployment manifests with replicas, probes, and resource limits
- Defining ClusterIP and LoadBalancer Services for network connectivity
- Separating configuration with ConfigMaps and sensitive data with Secrets
- Adding PersistentVolumeClaims for stateful workloads
- Applying hardened security contexts that run as non-root with dropped capabilities
- Organizing multi-resource manifests with Kustomize base-and-overlay structure

## Benefits
- Ship manifests that pass dry-run and linting on the first try
- Prevent resource starvation with requests and limits set by default
- Harden every pod with non-root, read-only filesystem, and dropped capabilities
- Keep environments consistent and DRY with shared labels and Kustomize overlays

## What’s included
- Deployment template with liveness, readiness, and resource configuration
- Service templates for ClusterIP and LoadBalancer exposure
- ConfigMap and Secret templates with proper separation of concerns
- PersistentVolumeClaim template and volume mount wiring
- Security context block with non-root, read-only root, and capability dropping
- Validation workflow using dry-run, kubeval, kube-score, and kube-linter

## Who it’s for
DevOps and platform engineers who need consistent, secure, production-grade Kubernetes manifests without hand-rolling YAML from scratch.

## How it runs
YAML gets written last. The workload is interrogated first, then every Deployment ships with probes, pinned tags and resource limits baked in, and three validators must pass before anything touches a cluster.
1. Interrogates the workload before writing YAML: stateless or stateful, image and exact tag, ports, CPU and memory needs, storage, external exposure and health endpoints. Missing answers become questions, not assumptions.
2. Writes the Deployment with the non-negotiables baked in: resource requests and limits, liveness plus readiness probes, pinned image tags (latest is forbidden), replica count matched to availability needs.
3. Picks the Service type deliberately: ClusterIP for internal traffic, LoadBalancer only when external exposure is justified, with named ports and selectors that provably match pod labels.
4. Separates configuration from secrets: ConfigMap for non-sensitive values, Secret for credentials with the explicit rule that plain secrets never land in Git, pointing to Sealed Secrets or External Secrets Operator instead.
5. Hardens the pod security context as a checklist: runAsNonRoot, drop ALL capabilities, readOnlyRootFilesystem, no privilege escalation, RuntimeDefault seccomp, plus the standard app.kubernetes.io label set on everything.
6. Validates before anything ships: kubectl apply --dry-run client and server side, then kubeval, kube-score and kube-linter, so a broken manifest fails in review instead of in the cluster.

## FAQ
### We use Helm. Does the Kustomize structure clash with that?
No. The templates are plain YAML; the Kustomize base-and-overlay layout is an organizational suggestion, not a dependency. Blocks like probes, resource limits, and the security context lift straight into your Helm charts.

### What makes these production-ready compared to what kubectl create gives me?
The defaults: every Deployment ships with resource requests and limits, liveness and readiness probes, a non-root security context with a read-only filesystem and dropped capabilities, and standard labels. A validation workflow with dry-run, kubeval, kube-score, and kube-linter targets manifests that pass linting on the first try.

### Does it generate CRDs, operators, or service mesh config?
No. The scope is core workloads: Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Mesh traffic policy and operator development are separate disciplines.

## Price
$15, one-time, no subscription. VAT included.

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